Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.3 KiB
Average record size in memory76.1 B

Variable types

Text2
Categorical3
Numeric5
DateTime1

Alerts

amount is highly overall correlated with log_amount and 1 other fieldsHigh correlation
log_amount is highly overall correlated with amount and 1 other fieldsHigh correlation
sqrt_amount is highly overall correlated with amount and 1 other fieldsHigh correlation
transaction_id has unique valuesUnique

Reproduction

Analysis started2026-02-21 12:32:55.632493
Analysis finished2026-02-21 12:33:05.463609
Duration9.83 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

transaction_id
Text

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2026-02-21T18:03:06.178600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st rowT000001
2nd rowT000002
3rd rowT000003
4th rowT000004
5th rowT000005
ValueCountFrequency (%)
t0000011
 
0.1%
t0000021
 
0.1%
t0000031
 
0.1%
t0000041
 
0.1%
t0000051
 
0.1%
t0000061
 
0.1%
t0000071
 
0.1%
t0000081
 
0.1%
t0000091
 
0.1%
t0000101
 
0.1%
Other values (990)990
99.0%
2026-02-21T18:03:07.190790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
03299
47.1%
T1000
 
14.3%
1301
 
4.3%
2300
 
4.3%
3300
 
4.3%
4300
 
4.3%
5300
 
4.3%
6300
 
4.3%
7300
 
4.3%
8300
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)7000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03299
47.1%
T1000
 
14.3%
1301
 
4.3%
2300
 
4.3%
3300
 
4.3%
4300
 
4.3%
5300
 
4.3%
6300
 
4.3%
7300
 
4.3%
8300
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03299
47.1%
T1000
 
14.3%
1301
 
4.3%
2300
 
4.3%
3300
 
4.3%
4300
 
4.3%
5300
 
4.3%
6300
 
4.3%
7300
 
4.3%
8300
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03299
47.1%
T1000
 
14.3%
1301
 
4.3%
2300
 
4.3%
3300
 
4.3%
4300
 
4.3%
5300
 
4.3%
6300
 
4.3%
7300
 
4.3%
8300
 
4.3%

user_id
Text

Distinct200
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2026-02-21T18:03:07.662038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st rowU0024
2nd rowU0196
3rd rowU0196
4th rowU0133
5th rowU0047
ValueCountFrequency (%)
u011713
 
1.3%
u014712
 
1.2%
u005212
 
1.2%
u002411
 
1.1%
u018211
 
1.1%
u011611
 
1.1%
u01419
 
0.9%
u00289
 
0.9%
u01509
 
0.9%
u00309
 
0.9%
Other values (190)894
89.4%
2026-02-21T18:03:08.474905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
01671
33.4%
U1000
20.0%
1720
14.4%
2222
 
4.4%
4209
 
4.2%
7207
 
4.1%
3203
 
4.1%
8200
 
4.0%
5194
 
3.9%
6190
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01671
33.4%
U1000
20.0%
1720
14.4%
2222
 
4.4%
4209
 
4.2%
7207
 
4.1%
3203
 
4.1%
8200
 
4.0%
5194
 
3.9%
6190
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01671
33.4%
U1000
20.0%
1720
14.4%
2222
 
4.4%
4209
 
4.2%
7207
 
4.1%
3203
 
4.1%
8200
 
4.0%
5194
 
3.9%
6190
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01671
33.4%
U1000
20.0%
1720
14.4%
2222
 
4.4%
4209
 
4.2%
7207
 
4.1%
3203
 
4.1%
8200
 
4.0%
5194
 
3.9%
6190
 
3.8%

product_id
Categorical

Distinct50
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
P042
 
31
P007
 
31
P023
 
30
P009
 
28
P024
 
26
Other values (45)
854 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP015
2nd rowP044
3rd rowP049
4th rowP042
5th rowP038

Common Values

ValueCountFrequency (%)
P04231
 
3.1%
P00731
 
3.1%
P02330
 
3.0%
P00928
 
2.8%
P02426
 
2.6%
P00825
 
2.5%
P02925
 
2.5%
P03725
 
2.5%
P00624
 
2.4%
P04924
 
2.4%
Other values (40)731
73.1%

Length

2026-02-21T18:03:08.709843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
p04231
 
3.1%
p00731
 
3.1%
p02330
 
3.0%
p00928
 
2.8%
p02426
 
2.6%
p00825
 
2.5%
p02925
 
2.5%
p03725
 
2.5%
p00624
 
2.4%
p04924
 
2.4%
Other values (40)731
73.1%

Most occurring characters

ValueCountFrequency (%)
01287
32.2%
P1000
25.0%
4320
 
8.0%
2313
 
7.8%
3296
 
7.4%
1265
 
6.6%
9119
 
3.0%
5106
 
2.6%
7103
 
2.6%
897
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01287
32.2%
P1000
25.0%
4320
 
8.0%
2313
 
7.8%
3296
 
7.4%
1265
 
6.6%
9119
 
3.0%
5106
 
2.6%
7103
 
2.6%
897
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01287
32.2%
P1000
25.0%
4320
 
8.0%
2313
 
7.8%
3296
 
7.4%
1265
 
6.6%
9119
 
3.0%
5106
 
2.6%
7103
 
2.6%
897
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01287
32.2%
P1000
25.0%
4320
 
8.0%
2313
 
7.8%
3296
 
7.4%
1265
 
6.6%
9119
 
3.0%
5106
 
2.6%
7103
 
2.6%
897
 
2.4%

amount
Real number (ℝ)

High correlation 

Distinct851
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.93868
Minimum19.53
Maximum132.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-21T18:03:08.934553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19.53
5-th percentile21.28
Q137.745
median56.39
Q376.92
95-th percentile123.1815
Maximum132.41
Range112.88
Interquartile range (IQR)39.175

Descriptive statistics

Standard deviation29.013313
Coefficient of variation (CV)0.48404992
Kurtosis-0.025561975
Mean59.93868
Median Absolute Deviation (MAD)19.355
Skewness0.78538545
Sum59938.68
Variance841.77235
MonotonicityNot monotonic
2026-02-21T18:03:09.182473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5753
 
5.3%
132.4131
 
3.1%
19.5331
 
3.1%
36.933
 
0.3%
59.753
 
0.3%
30.152
 
0.2%
38.622
 
0.2%
58.682
 
0.2%
74.372
 
0.2%
20.152
 
0.2%
Other values (841)869
86.9%
ValueCountFrequency (%)
19.5331
3.1%
19.571
 
0.1%
19.651
 
0.1%
19.661
 
0.1%
19.771
 
0.1%
19.781
 
0.1%
20.051
 
0.1%
20.061
 
0.1%
20.152
 
0.2%
20.231
 
0.1%
ValueCountFrequency (%)
132.4131
3.1%
132.321
 
0.1%
130.591
 
0.1%
130.411
 
0.1%
130.081
 
0.1%
129.831
 
0.1%
129.661
 
0.1%
129.641
 
0.1%
129.551
 
0.1%
128.031
 
0.1%

payment_type
Categorical

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Wallet
180 
UPI
177 
Cash
168 
Net Banking
165 
Debit Card
159 

Length

Max length11
Median length6
Mean length7.349
Min length3

Characters and Unicode

Total characters7349
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWallet
2nd rowUPI
3rd rowDebit Card
4th rowNet Banking
5th rowNet Banking

Common Values

ValueCountFrequency (%)
Wallet180
18.0%
UPI177
17.7%
Cash168
16.8%
Net Banking165
16.5%
Debit Card159
15.9%
Credit Card151
15.1%

Length

2026-02-21T18:03:09.443220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-21T18:03:09.616892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
card310
21.0%
wallet180
12.2%
upi177
12.0%
cash168
11.4%
net165
11.2%
banking165
11.2%
debit159
10.8%
credit151
10.2%

Most occurring characters

ValueCountFrequency (%)
a823
11.2%
e655
 
8.9%
t655
 
8.9%
C629
 
8.6%
475
 
6.5%
i475
 
6.5%
r461
 
6.3%
d461
 
6.3%
l360
 
4.9%
n330
 
4.5%
Other values (12)2025
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)7349
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a823
11.2%
e655
 
8.9%
t655
 
8.9%
C629
 
8.6%
475
 
6.5%
i475
 
6.5%
r461
 
6.3%
d461
 
6.3%
l360
 
4.9%
n330
 
4.5%
Other values (12)2025
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7349
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a823
11.2%
e655
 
8.9%
t655
 
8.9%
C629
 
8.6%
475
 
6.5%
i475
 
6.5%
r461
 
6.3%
d461
 
6.3%
l360
 
4.9%
n330
 
4.5%
Other values (12)2025
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7349
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a823
11.2%
e655
 
8.9%
t655
 
8.9%
C629
 
8.6%
475
 
6.5%
i475
 
6.5%
r461
 
6.3%
d461
 
6.3%
l360
 
4.9%
n330
 
4.5%
Other values (12)2025
27.6%

date
Date

Distinct636
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2023-01-01 00:00:00
Maximum2025-11-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-21T18:03:09.881901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:03:10.199241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

day
Real number (ℝ)

Distinct31
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.566
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2026-02-21T18:03:10.461519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q324
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.1066226
Coefficient of variation (CV)0.58503293
Kurtosis-1.2923437
Mean15.566
Median Absolute Deviation (MAD)8
Skewness0.0039781737
Sum15566
Variance82.930575
MonotonicityNot monotonic
2026-02-21T18:03:10.650297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2649
 
4.9%
1047
 
4.7%
844
 
4.4%
2242
 
4.2%
142
 
4.2%
341
 
4.1%
2340
 
4.0%
439
 
3.9%
2938
 
3.8%
236
 
3.6%
Other values (21)582
58.2%
ValueCountFrequency (%)
142
4.2%
236
3.6%
341
4.1%
439
3.9%
529
2.9%
631
3.1%
720
2.0%
844
4.4%
931
3.1%
1047
4.7%
ValueCountFrequency (%)
3121
2.1%
3025
2.5%
2938
3.8%
2826
2.6%
2730
3.0%
2649
4.9%
2536
3.6%
2429
2.9%
2340
4.0%
2242
4.2%

month
Real number (ℝ)

Distinct12
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.331
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2026-02-21T18:03:10.756570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2814947
Coefficient of variation (CV)0.5183217
Kurtosis-1.1373173
Mean6.331
Median Absolute Deviation (MAD)3
Skewness0.025847333
Sum6331
Variance10.768207
MonotonicityNot monotonic
2026-02-21T18:03:10.871015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5103
10.3%
10103
10.3%
697
9.7%
390
9.0%
889
8.9%
987
8.7%
484
8.4%
179
7.9%
276
7.6%
774
7.4%
Other values (2)118
11.8%
ValueCountFrequency (%)
179
7.9%
276
7.6%
390
9.0%
484
8.4%
5103
10.3%
697
9.7%
774
7.4%
889
8.9%
987
8.7%
10103
10.3%
ValueCountFrequency (%)
1256
5.6%
1162
6.2%
10103
10.3%
987
8.7%
889
8.9%
774
7.4%
697
9.7%
5103
10.3%
484
8.4%
390
9.0%

year
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023
350 
2024
337 
2025
313 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2025
4th row2024
5th row2025

Common Values

ValueCountFrequency (%)
2023350
35.0%
2024337
33.7%
2025313
31.3%

Length

2026-02-21T18:03:11.103829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-21T18:03:11.265255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2023350
35.0%
2024337
33.7%
2025313
31.3%

Most occurring characters

ValueCountFrequency (%)
22000
50.0%
01000
25.0%
3350
 
8.8%
4337
 
8.4%
5313
 
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
22000
50.0%
01000
25.0%
3350
 
8.8%
4337
 
8.4%
5313
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
22000
50.0%
01000
25.0%
3350
 
8.8%
4337
 
8.4%
5313
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
22000
50.0%
01000
25.0%
3350
 
8.8%
4337
 
8.4%
5313
 
7.8%

log_amount
Real number (ℝ)

High correlation 

Distinct942
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.053483
Minimum2.7472709
Maximum5.3933548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-21T18:03:11.452405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.7472709
5-th percentile3.1036894
Q13.6569987
median4.0498695
Q34.4300426
95-th percentile5.0484842
Maximum5.3933548
Range2.6460839
Interquartile range (IQR)0.77304387

Descriptive statistics

Standard deviation0.57500838
Coefficient of variation (CV)0.14185538
Kurtosis-0.37510149
Mean4.053483
Median Absolute Deviation (MAD)0.38553924
Skewness0.065426447
Sum4053.483
Variance0.33063463
MonotonicityNot monotonic
2026-02-21T18:03:11.635676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.74727091411
 
1.1%
5.39335478211
 
1.1%
4.1067670823
 
0.3%
3.6357423563
 
0.3%
3.0516399052
 
0.2%
3.6663781892
 
0.2%
4.1502521942
 
0.2%
4.3224093182
 
0.2%
4.0038726592
 
0.2%
3.1738784592
 
0.2%
Other values (932)960
96.0%
ValueCountFrequency (%)
2.74727091411
1.1%
2.7725887221
 
0.1%
2.7893229211
 
0.1%
2.8130106371
 
0.1%
2.8178010651
 
0.1%
2.8207834711
 
0.1%
2.8355635211
 
0.1%
2.8367365421
 
0.1%
2.8373225371
 
0.1%
2.8449093841
 
0.1%
ValueCountFrequency (%)
5.39335478211
1.1%
5.3839444531
 
0.1%
5.379205871
 
0.1%
5.3670968821
 
0.1%
5.3562088451
 
0.1%
5.3188060331
 
0.1%
5.3029565891
 
0.1%
5.2969163861
 
0.1%
5.2913934511
 
0.1%
5.2904363931
 
0.1%

sqrt_amount
Real number (ℝ)

High correlation 

Distinct902
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8179518
Minimum4.419276
Maximum13.120976
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-21T18:03:11.915094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.419276
5-th percentile4.613025
Q16.1436911
median7.5093265
Q39.1068652
95-th percentile12.441315
Maximum13.120976
Range8.7016996
Interquartile range (IQR)2.9631742

Descriptive statistics

Standard deviation2.2264269
Coefficient of variation (CV)0.28478391
Kurtosis-0.25677983
Mean7.8179518
Median Absolute Deviation (MAD)1.4583934
Skewness0.60784747
Sum7817.9518
Variance4.9569768
MonotonicityNot monotonic
2026-02-21T18:03:12.193050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.1209755731
 
3.1%
4.41927595931
 
3.1%
7.7298124173
 
0.3%
6.0770058423
 
0.3%
4.4888751372
 
0.2%
8.2746601142
 
0.2%
8.6238042652
 
0.2%
5.4909015652
 
0.2%
7.902531242
 
0.2%
6.7096944792
 
0.2%
Other values (892)920
92.0%
ValueCountFrequency (%)
4.41927595931
3.1%
4.4237992721
 
0.1%
4.4328320521
 
0.1%
4.4339598551
 
0.1%
4.4463468151
 
0.1%
4.4474711921
 
0.1%
4.4777226351
 
0.1%
4.4788391351
 
0.1%
4.4888751372
 
0.2%
4.4977772291
 
0.1%
ValueCountFrequency (%)
13.1209755731
3.1%
13.102671481
 
0.1%
12.99538381
 
0.1%
12.992305421
 
0.1%
12.975361271
 
0.1%
12.972663571
 
0.1%
12.867400671
 
0.1%
12.858460252
 
0.2%
12.81
 
0.1%
12.727136361
 
0.1%

Interactions

2026-02-21T18:03:00.783216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:02:56.925450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:02:58.037809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:02:58.976144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:02:59.919803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:03:00.999773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:02:57.209849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:02:58.224828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:02:59.199298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:03:00.061191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:03:01.220332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:02:57.340039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:02:58.406832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:02:59.422717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:03:00.251793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:03:01.635645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:02:57.505102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:02:58.563145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:02:59.651876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:03:00.407239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:03:02.625475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:02:57.725916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:02:58.755291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:02:59.796039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-21T18:03:00.569117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-21T18:03:12.388395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
amountdaylog_amountmonthpayment_typeproduct_idsqrt_amountyear
amount1.000-0.0120.9310.0130.0610.0450.9310.041
day-0.0121.000-0.013-0.0160.0280.000-0.0130.037
log_amount0.931-0.0131.0000.0410.0670.0001.0000.000
month0.013-0.0160.0411.0000.0000.0580.0410.171
payment_type0.0610.0280.0670.0001.0000.0730.0580.000
product_id0.0450.0000.0000.0580.0731.0000.0000.101
sqrt_amount0.931-0.0131.0000.0410.0580.0001.0000.000
year0.0410.0370.0000.1710.0000.1010.0001.000

Missing values

2026-02-21T18:03:03.726743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-21T18:03:05.006803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

transaction_iduser_idproduct_idamountpayment_typedatedaymonthyearlog_amountsqrt_amount
0T000001U0024P01567.67Wallet2023-02-1212220234.2293128.226178
1T000002U0196P04476.44UPI2023-03-2424320234.3495038.742997
2T000003U0196P049104.57Debit Card2025-08-2121820254.65937410.225947
3T000004U0133P042102.75Net Banking2024-07-2323720244.64198410.136567
4T000005U0047P03823.89Net Banking2025-10-0441020253.2144664.887740
5T000006U0024P03131.10UPI2025-04-1616420253.4688565.576737
6T000007U0086P02174.37Wallet2023-08-033820234.3224098.623804
7T000008U0042P02274.31Net Banking2025-10-11111020254.3216138.620325
8T000009U0074P04374.37Net Banking2023-03-1717320234.3224098.623804
9T000010U0117P00657.00UPI2024-05-3131520245.39335513.120976
transaction_iduser_idproduct_idamountpayment_typedatedaymonthyearlog_amountsqrt_amount
990T000991U0038P00458.16Wallet2024-08-2727820244.0802467.626270
991T000992U0053P04924.52Cash2023-03-088320233.2394624.951767
992T000993U0092P02938.06Credit Card2024-04-044420243.6650996.169279
993T000994U0191P04266.15Cash2025-03-077320254.2069298.133265
994T000995U0061P03320.99Wallet2024-08-1212820243.0905884.581484
995T000996U0178P04371.11Credit Card2025-02-2020220254.2781938.432675
996T000997U0100P02753.96Wallet2024-10-0221020244.0066067.345747
997T000998U0142P00476.06Credit Card2024-05-2929520244.3445848.721238
998T000999U0052P04062.45Net Banking2024-04-066420244.1502527.902531
999T001000U0163P046123.78Wallet2025-01-2323120254.82655211.125646